A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
2026-05-25 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created PulmoFoundation, an AI model designed to help doctors analyze lung tissue samples at different stages of diagnosis and treatment. They trained and tested it on tens of thousands of images from multiple medical centers and showed it can accurately identify important diagnostic and molecular features. In real patient studies, the model reduced extra work for pathologists and improved their accuracy, confidence, and agreement on diagnoses while also saving time. Their work shows PulmoFoundation can be a useful tool to support lung cancer diagnosis in clinical practice.
foundation modellung pathologywhole-slide imagesdiagnostic accuracyprospective validationrandomized controlled trialimmunohistochemistry (IHC)frozen sectionbiopsyarea under the curve (AUC)
Authors
Zhengrui Guo, Zhengyu Zhang, Jiabo Ma, Yihui Wang, Fengtao Zhou, Yingxue Xu, Ling Liang, Chenglong Zhao, Qi Xie, Jinbang Li, Shujing Guo, Fangyi Han, Zhijian Cen, Ziyi Liu, Cheng Jin, Junlin Hou, Zhixuan Chen, Yu Cai, Lijuan Qu, Shifu Chen, Yueping Liu, Zhe Wang, Xiuming Zhang, Muyan Cai, Li Liang, Hao Chen
Abstract
Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objectives. Although pan-cancer foundation models offer versatility, they lack subspecialty-level depth and have not been evaluated across clinical workflows or prospectively validated in real-world settings. We introduce PulmoFoundation, a multi-center, prospectively validated, randomized controlled trial (RCT)-evaluated foundation model for comprehensive lung pathology assessment across pre-operative, intra-operative, and post-operative care. Built upon Virchow2 via subspecialty-specific pretraining using ~40,000 diagnostic H&E-stained whole-slide images (WSIs), PulmoFoundation was systematically evaluated on ~26,000 WSIs across 32 clinically relevant tasks. In addition to accurately predicting molecular markers and patient survival, our model achieves clinical-grade performance in core diagnostic tasks across biopsy, frozen section, and surgical resection slides. In a registered prospective study of 1,357 patients across 11 diagnostic tasks, our model achieved an average AUC of 92.3%. Using pre-specified triage thresholds, PulmoFoundation could reduce additional second-review burden for 68.8% of biopsies and 83.0% of frozen sections, and defer 44.5% of IHC stain orders, with PPVs of 1.0, 0.991, and 0.966. Beyond prospective validation, we conducted a crossover RCT with eight pathologists, in which AI assistance improved diagnostic accuracy across 4,928 case-reader pairs (91.7% w/ AI vs. 83.8% w/o AI). AI assistance also reduced median diagnostic time by 19.6%, increased diagnostic confidence by 8.7%, and improved inter-rater agreement from moderate (kappa = 0.56) to substantial (kappa = 0.76). Together, these evaluations support PulmoFoundation as a clinically validated decision-support system for lung pathology.